We believe that children as young as ten can directly benefit from opportunities to engage in computational thinking. One approach to provide these opportunities is to focus on social game play. Understanding game play is common across a range of media and ages. Children can begin by solving puzzles on paper, continue on game boards, and ultimately complete their solutions on computers. Through this process, learners can be guided through increasingly complex algorithmic thinking activities that are built from their tacit knowledge and excitement about game play. This paper describes our approach to teaching computational thinking skills without traditional programming -but instead by building on children's existing game playing interest and skills. We built a system called CTArcade, with an initial game (TicTac-Toe), which we evaluated with 18 children aged 10-15. The study shows that our particular approach helped young children to better draw out and articulate algorithmic thinking patterns, which were tacitly present when they played naturally on paper, but not explicitly apparent to them until they used the CTArcade interface.
Probabilistic topic models are important tools for indexing, summarizing, and analyzing large document collections by their themes. However, promoting end-user understanding of topics remains an open research problem. We compare labels generated by users given four topic visualization techniques—word lists, word lists with bars, word clouds, and network graphs—against each other and against automatically generated labels. Our basis of comparison is participant ratings of how well labels describe documents from the topic. Our study has two phases: a labeling phase where participants label visualized topics and a validation phase where different participants select which labels best describe the topics’ documents. Although all visualizations produce similar quality labels, simple visualizations such as word lists allow participants to quickly understand topics, while complex visualizations take longer but expose multi-word expressions that simpler visualizations obscure. Automatic labels lag behind user-created labels, but our dataset of manually labeled topics highlights linguistic patterns (e.g., hypernyms, phrases) that can be used to improve automatic topic labeling algorithms.
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